The activity of sinus node pacemaker cells is under continuous regulation mainly effected by neural mechanisms. Hence the study of the variability of heart period, especially when assessed in the frequency domain with spectral analysis, was proposed about 15 years ago [l] as a probe for the evaluation of its autonomic control. Initially three spectral components of heart rate variability (HRV) were defined, while the factors considered as modulating them included sympathetic and parasympathetic activities, humoral factors such as the renin-angiotensin system or complex patterns like thermoregulation. On the basis of (i) an attempt to analyse cardiovascular neural regulation in closed-loop conditions, (ii) a different spectral methodology using autoregressive algorithms [21, (iii> simultaneous spectral analysis of both heart rate and arterial pressure variabilities providing the possibility of calculating crossspectra and their coherence, we reached the conclusion that only two spectral components were soundly analysable in a time span of a few hundred cycles-i.e., the time series usually selected to afford an adequate number of events, adequate stationarity and some appraisal of dynamic changes [3]. Hence, apart from a very low frequency (VLF) component [41, short-term variability appeared to consist mainly of an oscillation at low frequency (LF), usually around 0.1 Hz largely related to vasomotor activity, and of a high frequency (HF) oscillation, related to respiratory activity and under control conditions often around 0.25 Hz. Since our initial studies [2], we observed that the power of the LF component was percentually increased during manoeuvres exciting sympathetic activity while HF was

Heart rate variability and its sympatho-vagal modulation

A time-variant algorithm of autoregressive (AR) identification is introduced and applied to the heart rate variability (HRV) signal. The power spectrum is calculated from the AR coefficients derived from each single RR interval considered. Time-variant AR coefficients are determined through adaptive parametric identification with a forgetting factor which obtains weighted values on a running temporal window of 50 preceding measurements. Power spectrum density (PSD) is hence obtained at each cardiac cycle, making it possible to follow the dynamics of the spectral parameters on a beat-by-beat basis. These parameters are mainly the LF (low-frequency) and the HF (high-frequency) powers, and their ratio, LF/HF. These together account for the balanced sympatho-vagal control mechanism affecting the heart rate. This method is applied to subjects suffering from transient ischemic attacks. The time variant spectral parameters suggest an early activation of LF component in the HRV power spectrum. It precedes by approximately 1.5-2 min the tachycardia and the ST displacement, generally indicative of the onset of an ischemic episode. >

Time-variant power spectrum analysis for the detection of transient episodes in HRV signal

Power spectral analysis of heart period (R-R) and arterial pressure short-term variability reveals the existence of two rhythmic oscillations: a high frequency (HF) component corresponding to respiratory activity (around 0.25 Hz) and a low frequency (LF) component corresponding to vasomotor waves (around 0.10 Hz). Further, a very low frequency (VLF) component, with a center frequency around 0 Hz, can be identified. It is generally accepted that the power of the HF component of R-R variability is a marker of vagal activity to the sinoatrial node. We have introduced the hypothesis, which is becoming progressively accepted, that the LF component of R-R variability is a marker of sympathetic modulation of the sinoatrial node, whereas the LF component of arterial pressure variability is a marker of sympathetic modulation of vasomotor tone. Moreover, we have suggested that the evaluation (in normalized units) of the LF and HF components of R-R variability (or the LF/HF ratio) can assess the state of the sympatho-vagal balance modulating the sino-atrial node in numerous physiologic and physiopathologic conditions. A large body of evidence has been gathered on the basis of multiple recordings of cardiovascular signals obtained in humans and in acute and chronic animal experiments in favor of this hypothesis.

Physiology and clinical implications of variability of cardiovascular parameters with focus on heart rate and blood pressure.

The time series of successive heart periods present important variations around its mean value, determining the phenomenon of heart rate variability (HRV), assessed with both time and frequency domain approaches. A low standard deviation of the heart period (a time domain index of HRV) is a powerful prognostic indicator of sudden coronary death in patients recovering from acute myocardial infarction. Spectral analysis of HRV usually demonstrates two major components: indicated as LF (low frequency, approximately 0.1 Hz) and HF (high frequency, approximately 0.25 Hz). They are defined by center frequency and associated power, which is expressed in msec2 or normalized units. When assessed in normalized units, LF and HF provide quantitative indicators of neural control of the sinoatrial node. Numerous experimental and clinical studies have consistently indicated that the LF component is a marker of sympathetic modulation and HF a marker of vagal modulation; the LF/HF ratio is a synthetic index of sympathovagal balance. In the analysis of 24-hour Holter recordings of normal subjects, a circadian rhythmicity of spectral markers of sympathetic and vagal modulation is clearly present, with a sympathetic predominance during the day and a vagal predominance during the night. In patients recovering from an acute myocardial infarction, spectral analysis of HRV revealed an increased sympathetic and decreased vagal activity during early convalescence, and a return to their normal balance by 6 to 12 months. A clear increase of LF was also evident in patients studied within a few hours of the onset of symptoms related to an acute myocardial infarction, independent of its location. Similarly, LF increased during transient myocardial ischemia. An increase in markers of sympathetic activity has also been observed prior to episodes of malignant arrhythmias. Spectral analysis of HRV could help in the understanding of the role of abnormal neural mechanisms in sudden coronary death, thus contributing to its prevention.

Power spectral analysis of cardiovascular variability in patients at risk for sudden cardiac death.

Various algorithms of autoregressive (AR) recursive identification make it possible to evaluate power spectral distribution in correspondence with each sample of a time series, and time-variant spectral parameters can be calculated through the evaluation of the pole positions in the complex z-plane. In traditional analysis, the poles are obtained by zeroing the denominator of the model transfer function, expressed as a function of the AR coefficients. Here, two algorithms for the direct updating and tracking of movements of poles of an AR time-variant model on the basis of the innovation given to the coefficients are presented and investigated. The introduced algorithms are based upon (1) the classical linearization method and (2) a recursive method to compute the roots of a polynomial, respectively. Here, applications in the field of heart rate variability (HRV) signal analysis are presented and efficient tools are proposed for quantitative extraction of spectral parameters (power and frequency of the low-frequency (LF) and high-frequency (HF) components) for the monitoring of the action of the autonomic nervous system in transient pathophysiological events. These computational methods seem to be very attractive for HRV applications, as they inherit the peculiarity of recursive time-variant identification, and provide a more immediate comprehension of the spectral process characteristics when expressed in terms of poles and AR spectral components. >

Pole-tracking algorithms for the extraction of time-variant heart rate variability spectral parameters

The authors describe some time-variant algorithms of autoregressive (AR) identification that result in obtaining a set of AR parameters for each data sample. The performances of the algorithms were tested on simulated series to better understand their capability in tracking abrupt or constant-rate changes in the signal. A power spectrum density was obtained at each sample and a compressed spectrum array (CSA) graph is plotted. The algorithms were then applied in the study of the heart rate variability signal in dogs during coronary occlusion and in human subjects during transient ischemic episodes. Spectral parameters were obtained on a beat-to-beat basis, for a better comprehension of the dynamic role of the autonomic nervous system in this pathology. >

A. Montefusco

Papers

Time-variant spectral estimation of heart rate variability signal